Results in Materials最新文献

It was considered the forming multilayer heat-resistant coatings that work under conditions of elevated temperatures and long-term oxidation. The issue of increasing the service life of the parts by strengthening their bulk and surface is considered. The issue of determining the level of the thermophysical properties is also considered. The thermophysical properties of carbonyl iron reinforced with carbon fibers (CF) pre-clad with nickel are investigated. Unclad and nickel clad carbon fibers were produced. The thermal conductivities of the samples are determined depending on the conditions of their production. The thermal conductivity characteristics of polymer composite materials based on CF with ceramic hollow microspheres and carbonyl iron from CF depending on the conditions of their production were determined. It is shown that heat transfer mechanisms related to the electron-lattice interaction can function in these systems. Heat transfer can occur by various transport mechanisms, such as collisions or diffusion. Nevertheless, the increase in thermal conductivity can be related to the electronic mechanism. The thermal conductivity of materials based on carbonyl iron with nickel-plated CF is 1.2 times higher compared to unclad CF, which is due to better adhesive interaction between the coated carbon fibers and iron was established.

A surface modification process for steel rods based on atmospheric-pressure nitrogen plasma (APP) at room temperature was developed. Steel rods were irradiated with APP generated by dielectric-barrier discharge in a nitrogen atmosphere. Elemental analysis using an electron-probe microanalyzer revealed strong nitrogen signals at the top surface, which suggests the formation of a modified layer due to the generation of excited nitrogen sources by APP. However, no noticeable nitrogen diffusion into the steel was observed. This is because the excited nitrogen sources reacted with elemental iron emitted by APP and nitrides were deposited on the surface of the steel. The proposed APP treatment is a promising approach for modifying the surface of steel rods without heat treatment.

The recycling of polyvinylchloride (PVC) from waste electrical cables in blends with low-density polyethylene (LDPE) is here explored, focusing on the material's mechanical and physical responses. Factors influencing these responses were identified, and reasonable ranges of variability were estimated for each factor. A comprehensive test plan was subsequently devised to develop optimal PVC/LDPE blends. The samples were produced by melt-blending the polymers in a counter-rotating internal mixer followed by compression molding. The effects of suitable coupling additives were also evaluated to enhance phases adhesion. The study investigated the impact of various processing parameters on thermomechanical properties using ANOVA, while a specific focus on optimizing material toughness was pursued. This optimization aimed to maximize both elastic modulus and elongation at break, through the application of genetic algorithms. Introducing a 5 % compatibilizer significantly enhances the interface, resulting in a complex fracture surface facilitated by its presence. Higher mixing temperatures promote better dispersion and distribution of PVC and the compatibilizer within the LDPE matrix, yielding a more uniform and interconnected structure. Increased PVC content correlates with reduced elastic modulus in the blend. The inclusion of a compatibilizer plays a vital role in counteracting the negative effects of PVC particles on stiffness, acting as a bridge to enhance interactions with the matrix and thereby improving interfacial adhesion and overall structure. The study offers insights into enhancing the recyclability of PVC from waste cables and optimizing the mechanical performance of the resultant materials.

In the present work, we have made a comparative investigation on the structural, elastic, electronic, thermos-physical, and optical properties of cubic binary lave phase intermetallic compound with the common formula HfX₂ (X = Cr, Mo and W) based on Density Functional Theory (DFT) using CASTEP. The result of geometrical optimization has been strongly agreed with the experimental results. The HfW₂ compound exhibits larger lattice parameters and volumes than HfMo₂ and HfCr₂ because of the variation of atomic radii of the exhibiting elements in the studied compounds. The elastic properties show that each of the compounds in the ground state is mechanically stable and has a highly ductile nature. Band structures and energy densities of states have been studied to learn more about electronic properties. These compounds display metallic properties in their electrical band structures. They also have high machinability, a low Debye temperature, low bond hardness, and a remarkably high melting point. Our investigation thoroughly examined the reflectivity, absorption coefficient, refractive index, dielectric function, optical conductivity, and loss function of these metals. The optical absorption, reflectivity spectra, and refractive index of HfX₂ (X = Cr, Mo, and W) indicate their potential for use as solar reflectors in the IR-Vis region and UV–Vis absorbers.

High-stiffness and high-strength natural fibres are commonly utilized in thermoplastic matrices to manufacture biobased thermoplastic composites. Low-end applications such as construction and furniture industries do not require a high load-bearing capacity, thus favoring cost-effective, moderately strong, sustainable natural materials and quickly manufacture thermoplastic composites. Recycled biobased cotton fibres collected from the denim industry can effectively penetrate this composite industry due to their cost-effectiveness and desirable physical and mechanical properties. However, retaining structural integrity and enhancing the reinforcing efficiency of recycled yarns in composites pose challenges. Addressing these issues, we propose a new method of extracting recycled cotton from denim waste fabric and transforming it into dry preform with minimal damage to the structural integrity of cotton fibres. Jute fibres have been introduced alongside recycled denim fibers within a polypropylene matrix to optimize the hybrid reinforcing efficacy of the composites. The effect of denim fibre length on composite quasi-static tensile and flexural test results showed that a yarn length of 20 mm yielded the highest tensile strength (⁓27.83 MPa), tensile modulus (⁓ 1.76 GPa), flexural strength (⁓62.35 MPa), and flexural modulus (⁓1.17 GPa). Composites comprising recycled denim yarn and jute fibre improved the tensile modulus to approximately ⁓2.1 GPa and the impact strength to approximately ⁓66.1 kJ/m², following the hybrid rule of mixture, while jute fiber serves to increase the stiffness and toughness of the composites. This work demonstrates that recycled denim cotton fibre and strong jute fibre can be successfully integrated into thermoplastic composites to achieve tailored mechanical properties.

When designing against fatigue, the surface condition of steel is of utmost importance. Since hot-dip galvanizing is a surface treatment primarily aimed at preventing corrosion, it is critical to assess its influence on the fatigue properties of steel. Given the challenges in producing large structural galvanized steel without imperfections, it is crucial to understand the influence of manufacturing-induced microscopic surface defects on crack initiation and fatigue mechanisms in hot-dip galvanized steel. In this study, to gain such insights, tension-tension fatigue testing was conducted on hot-dip galvanized steel, followed by a detailed analysis of the crack initiation sites to determine any correlation with manufacturing-induced microscopic surface defects. It was observed that under low-cycle fatigue, pre-existing surface defects did not appear to have influenced fatigue mechanisms. However, under high-cycle fatigue, crack initiation sites exhibited evidence of pre-existing manufacturing induced defects which were significantly smaller than the total crack initiation area. This indicates that the defects could not immediately penetrate the substrate but expanded within the layer and only penetrated the substrate after reaching a critical size. Therefore, it is discovered that while manufacturing-induced defects may be challenging to eradicate entirely, those below a threshold size may not immediately evolve into a stable fatigue crack. Hence, if manufacturing induced defects are kept below a critical size, they may not significantly influence the transition of stage I crack into a stable stage II crack.

This work investigated the effect of a victorious parameter of electrocoagulation (EC) on the removal of turbidity through batch reactor using Fe and Al electrodes. Synthetic (Sol 1, Sol 2) and natural wastewater (Sol 3) with different sodium chloride content were examined under different current densities (CD) and operating time. For the examined solutions conductivity, pH, total suspended solids (TSS) dissolved oxygen (DO) and energy consumption were measured. A novel approach examined the effect of solutions with selected operating parameters on Fe and Al corrosion using chemical and electrochemical tests. Using experimental data collected during the various parameters, a mathematical model of the electrocoagulation process was generated and assessed. FE-SEM images and EDX analyses for Fe and Al electrodes and XRD of coagulant particles, floc, generated by EC were also investigated. According to the findings, the ideal circumstances for the turbidity elimination from the examined solution were found to be as CD = 11 mA/cm², pH ⁓ 7, and operating time = 20 min. Under these optimal circumstances, Al electrodes showed superior efficacy in removing than Fe electrodes for all examined solutions. The turbidity removal efficiency and the energy consumption were also found to be affected by NaCl content. The turbidity removal efficiencies reached their maximum value of 99 % for sol 3 where NaCl content was 3.45 × 10⁻¹ M and energy consumption was 1.667 kWh/m³ by using Al anode.

The High Entropy Alloy (HEA) coatings exhibit diverse properties contingent upon their composition and microstructure, addressing current industrial requirements. Machine Learning (ML) regression emerges as a proficient solution for predicting the properties of HEA coatings, offering a significant reduction in experimental work. The ML regressions including Support Vector Regression (SVR), Gaussian Process Regression (GPR), Ridge Regression (RR), and Polynomial Regression (PR), are effectively employed to predict Young's modulus of HEA coated Stainless Steel (SS) through a significant database. The statistical responses of the developed regression models are analyzed through evaluation indices of Coefficient of determination (R²), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). Among the regression models, the 2-degree PR model stands alone with a high prediction accuracy of R²-0.95, MAE-16.12, and RMSE-21.53. The 2-degree PR model demonstrates a significant correlation between the predicted and experimental Young's modulus, contributing to the accurate prediction of unknown Young's modulus of the HEA-coated SS. The prediction of Young's modulus by the PR model is more reliable, as proved by an error percentile of ±4.76 %, compared to the experimental values of Young's modulus.

Mixing is an often overlooked aspect in the concrete production process, yet, it has a major impact on the concrete characteristics. This research investigates how different mixing methods affect the hardened concrete characteristics across four distinct mix designs. A primary objective of the study is to assess the compressive strength of concrete, which serves as a crucial indicator of its structural integrity. This is evaluated at 7, 28, and 56 days of curing. Three distinct experimental testing series were conducted to comprehensively assess the impact of various mixing methods on concrete performance. The findings indicate that higher shear rates during mixing can elevate the development of strength, depending on the type of additives utilized and the specific curing period. Limitations to such improvements were briefly discussed as well. Of particular note is the finding that selecting the optimal mixing method could potentially result in a reduction in cement clinker content while maintaining concrete performance.

The aim of this study is to propose a model for predicting the mechanical properties of natural sisal yarn based on mechanical strength of used fibers, unit twist of yarn and its density. To achieve this, a complete factorial design modelling method was used, based on a design of experiments approach, which is recommended today because of the ease it offers in taking into account the most complex phenomena. It was developed on the basis of laboratory tests. The model obtained predicts mechanical strength and elasticity modulus of sisal yarn, based on control parameters such as fiber strength, unit twist and yarn mass. This model is currently one of the most accurate tools for predicting and optimising the mechanical properties of sisal yarns. The iso-surface curves represented by this model, can be used to obtain the strength and elasticity modulus of sisal yarn during manufacture, by varying the optimum control parameters. This model is an original tool that can be recommended to the spinning and sisal fiber composite manufacturing industries and others, as it saves time and materials and ensures precision in the manufacture of yarns according to the field of application, thereby contributing to safety in use.